306,013 research outputs found
Variant interpretation through Bayesian fusion of frequency and genomic knowledge
Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation
The moving crowd: collecting and processing of crowd behaviour data
The MOVE project focuses on the collection and analyses of crowd behavior data. The two main goals of the project are first, the collection of data through mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as interprete the dynamic behaviour of the population
Link between K-absorption edges and thermodynamic properties of warm-dense plasmas established by improved first-principles method
A precise calculation that translates shifts of X-ray K-absorption edges to
variations of thermodynamic properties allows quantitative characterization of
interior thermodynamic properties of warm dense plasmas by X-ray absorption
techniques, which provides essential information for inertial confinement
fusion and other astrophysical applications. We show that this interpretation
can be achieved through an improved first-principles method. Our calculation
shows that the shift of K-edges exhibits selective sensitivity to thermal
parameters and thus would be a suitable temperature index to warm dense
plasmas. We also show with a simple model that the shift of K-edges can be used
to detect inhomogeneity inside warm dense plasmas when combined with other
experimental tools
Interpretation on Multi-modal Visual Fusion
In this paper, we present an analytical framework and a novel metric to shed
light on the interpretation of the multimodal vision community. Our approach
involves measuring the proposed semantic variance and feature similarity across
modalities and levels, and conducting semantic and quantitative analyses
through comprehensive experiments. Specifically, we investigate the consistency
and speciality of representations across modalities, evolution rules within
each modality, and the collaboration logic used when optimizing a
multi-modality model. Our studies reveal several important findings, such as
the discrepancy in cross-modal features and the hybrid multi-modal cooperation
rule, which highlights consistency and speciality simultaneously for
complementary inference. Through our dissection and findings on multi-modal
fusion, we facilitate a rethinking of the reasonability and necessity of
popular multi-modal vision fusion strategies. Furthermore, our work lays the
foundation for designing a trustworthy and universal multi-modal fusion model
for a variety of tasks in the future.Comment: This version was under review since 2023/3/
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
The joint analysis of biomedical data in Alzheimer's Disease (AD) is
important for better clinical diagnosis and to understand the relationship
between biomarkers. However, jointly accounting for heterogeneous measures
poses important challenges related to the modeling of the variability and the
interpretability of the results. These issues are here addressed by proposing a
novel multi-channel stochastic generative model. We assume that a latent
variable generates the data observed through different channels (e.g., clinical
scores, imaging, ...) and describe an efficient way to estimate jointly the
distribution of both latent variable and data generative process. Experiments
on synthetic data show that the multi-channel formulation allows superior data
reconstruction as opposed to the single channel one. Moreover, the derived
lower bound of the model evidence represents a promising model selection
criterion. Experiments on AD data show that the model parameters can be used
for unsupervised patient stratification and for the joint interpretation of the
heterogeneous observations. Because of its general and flexible formulation, we
believe that the proposed method can find important applications as a general
data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with
MICCAI 2018, September 20, Granada, Spai
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